| 基于T213集合预报的中国极端温度预报方法研究 |
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| 引用本文： | 吴剑坤,高丽,乔林,陈静.基于T213集合预报的中国极端温度预报方法研究[J].气象科学,2015,35(4):438-444. |
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| 作者姓名： | 吴剑坤 高丽 乔林 陈静 |
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| 作者单位： | 北京市气象台, 北京 100089,中国气象局 数值预报中心, 北京 100081,北京市气象台, 北京 100089,中国气象局 数值预报中心, 北京 100081 |
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| 基金项目： | 北京市气象局科研专项(2012BMBKYZX10);中国气象局预报员专项(CMAYBY2014-001) |
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| 摘 要： | 基于中国T213集合预报系统资料,根据Anderson-Darling检验原理,研究基于集合预报与模式历史预报累积概率密度(简称模式气候)分布函数连续差异特征的极端温度天气预报方法,建立极端温度天气预报指数(Extreme Temperature Forecast Index, 简称EFI)的数学模型。利用S指数评分方法确定发布极端温度预警信号的阈值,得出:1月的发布极端高温的预警信号的阈值为0.7或0.8,发布极端低温的预警信号的阈值为-0.7或-0.8。基于EFI指数以及该阈值,对2013年1月中国极端温度天气进行预报试验,得出:极端天气预报指数对极端温度天气具有较好的识别能力,可提前3~7 d发出极端温度预警信号,随着预报时效的延长,预报技巧逐渐降低。
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| 关 键 词： | 极端高温 极端低温 极端天气预报指数 模式气候累积概率 T213集合预报 |
| 收稿时间： | 2013/11/12 0:00:00 |
| 修稿时间： | 7/7/2014 12:00:00 AM |
Research on Chinese extreme temperature forecasting method based on T213 ensemble forecast |
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| WU Jiankun,GAO Li,QIAO Lin and CHEN Jing.Research on Chinese extreme temperature forecasting method based on T213 ensemble forecast[J].Scientia Meteorologica Sinica,2015,35(4):438-444. |
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| Authors: | WU Jiankun GAO Li QIAO Lin and CHEN Jing |
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| Institution: | Beijing Meteorological Observatory, Beijing 100089, China,Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081, China,Beijing Meteorological Observatory, Beijing 100089, China and Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081, China |
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| Abstract: | Based on the T213 global ensemble prediction system (EPS) from China Meteorological Administration(CMA), according to the principle of Anderson-Darling test, a extreme temperature forecasting method combining the cumulative distribution functions (CDF) derived from climate and the EPS forecast is analyzed, and then a new ensemble forecast product:Extreme Temperature Forecast Index (EFI) is established. The EFI threshold by S index for extreme high temperature is 0.7 or 0.8, and for extreme low temperature, it is-0.7 or-0.8. Several extreme temperature events in January, 2013 were forecasted and tested by EFI index and its threshold. The results showed that the EFI is able to identify extreme temperatures within 3-7 days and to provide early warnings on extreme temperature. |
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| Keywords: | Extreme high temperature Extreme low temperature Extreme forecast index Model climate cumulative distribution T213 ensemble forecast |
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